Freqai-Strategy

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Freqai-Strategy

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# FreqAI Strategy - AI-Driven Trading with LSTM

![FreqAI](https://www.freqtrade.io/en/stable/assets/freqai_algo.jpg)

## Overview
**FreqAI-Strategy** is an advanced **AI-driven trading strategy** built for [Freqtrade](https://www.freqtrade.io/en/stable/) using **Long Short-Term Memory (LSTM) neural networks**. This strategy is designed to:
- **Predict future price trends** in cryptocurrency markets using deep learning.
- **Execute trades based on AI-generated signals** to maximize profitability.
- **Adapt dynamically to market conditions** using engineered features.

🚨 **Work in Progress:** This strategy is still under active development and **is not meant for live trading with real money**. Use it for research and backtesting only.

## Features
✅ **Uses LSTM for time-series forecasting**  
✅ **Dynamic target scaling and market regime filtering**  
✅ **Backtesting and hyperparameter tuning support**  
✅ **Supports multiple timeframes (1h, 2h, 4h)**  
✅ **Automated model training and retraining**  
✅ **Optimized for Binance Futures Trading**

## Installation
### 1️⃣ Install Freqtrade
```bash
# Clone and install Freqtrade
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
./setup.sh --install
```

### 2️⃣ Install Required Dependencies
```bash
pip install -r requirements.txt
```

### 3️⃣ Clone This Repository
```bash
git clone https://github.com/GoodyNick/Freqai-Strategy.git
cd Freqai-Strategy
```

### 4️⃣ Copy Paths for Configuration and Model Files
After cloning, ensure the configuration, strategy, and model files are correctly placed in the Freqtrade directory structure. Use the following commands:
```bash
# Copy configuration file
cp config-torch-lstm_v2.json /freqtrade/user_data/configs/

# Copy strategy file
cp ExampleLSTMStrategy_v2.py /freqtrade/user_data/strategies/

# Copy model-related files
cp PyTorchLSTMModel_v2.py /freqtrade/freqtrade/freqai/torch/
cp PyTorchLSTMRegressor_v2.py /freqtrade/user_data/freqaimodels/
cp PyTorchModelTrainer_v2.py /freqtrade/freqtrade/freqai/torch/
cp freqai_interface.py /freqtrade/freqtrade/freqai/
```
Modify them as needed before running Freqtrade.

## Configuration
Modify `config-torch-lstm_v2.json` to customize:
- **Train/Test periods** (`train_period_days`, `backtest_period_days`)
- **Feature Engineering Parameters** (DI threshold, scaling methods)
- **LSTM Model Parameters** (`hidden_dim`, `num_lstm_layers`, `dropout`, etc.)
- **Trading Settings** (Max trades, margin mode, stake size)
- **model training parameters**
- ** ... ***

## Running Backtests
```bash
freqtrade backtesting --config config-torch-lstm_v2.json --strategy ExampleLSTMStrategy_v2
```
you can also use run.sh script for backtesting, plotting, or hyperopt freqai strategy

## Contributions & Contact
🤝 **Contributions are welcome!** If you have suggestions or improvements, feel free to submit a **pull request** or open an **issue**.

📬 **Contact:** [GitHub Issues](https://github.com/GoodyNick/Freqai-Strategy/issues) or reach out on Discord!

---
**License:** MIT

Freqai-Strategy

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